Evidence map›Paper›PMID 30058111›Full record

ArticleProteomics. Clinical applications2019

Using Stepwise Pharmacogenomics and Proteomics to Predict Hepatitis C Treatment Response in Difficult to Treat Patient Populations.

Susanna Naggie, Meredith Clement, Sam Lusk, Anu Osinusi, Tiffany Himmel, Joseph E Lucas, Will J Thompson, Laura Dubois, M Arthur Moseley, Paul J Clark and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proteomics. Clinical applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Susanna NaggieInfectious Diseases, Duke Clinical Research Institute, Durham, NC, USA.ORCID 0000-0001-7721-6975
Meredith ClementInfectious Diseases, Duke Clinical Research Institute, Durham, NC, USA.
Sam LuskInfectious Diseases, Duke Clinical Research Institute, Durham, NC, USA.
Anu OsinusiGilead Sciences, Inc, Foster City, CA, USA.
Tiffany HimmelInstitute for Genome Sciences and Policy, Duke University, Durham, NC, USA.
Joseph E LucasInfectious Diseases, Duke Clinical Research Institute, Durham, NC, USA.
Will J ThompsonDuke Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.
Laura DuboisDuke Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.
M Arthur MoseleyDuke Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.
Paul J ClarkInfectious Diseases, Duke Clinical Research Institute, Durham, NC, USA.
Shyam KottililUniversity of Maryland School of Medicine, Baltimore, MD, USA.
Keyur PatelInfectious Diseases, Duke Clinical Research Institute, Durham, NC, USA.

Funding

Social and Behavioral Sciences CoreP30AI064518 · NIAID · DUKE UNIVERSITY · PI GEORGIA Doris TOMARAS · 2005 to 2026
$50.5M
Ruth L. Kirschstein National Research Service Award (NRSA)- T32T32AI007392 · NIAID · DUKE UNIVERSITY · PI Amy Lynn Corneli, Guido Ferrari · 1990 to 2026
$9.8M
Identification of Novel Bioactive Lipid Metabolites for Predicting Liver-related Outcomes in Persons Co-Infected with HIV and HCVR01DK112295 · NIDDK · DUKE UNIVERSITY · PI NAGGIE, SUSANNA · 2017 to 2021
$2.0M
Moving Towards More Individualized Therapies in HIV/HCV Co-infected PatientsK23AI096913 · NIAID · DUKE UNIVERSITY · PI NAGGIE, SUSANNA · 2011 to 2014
$355k
Intramural NIH HHSNIAID NIH HHS K23 AI096913NIAID NIH HHS K23-AI096913NIAID NIH HHS P30 AI064518NIAID NIH HHS T32 AI007392NIDDK NIH HHS 1R01DK112295-01NIDDK NIH HHS R01 DK112295NIH HHS HSN261200800001E
6 · The paper itself

Abstract

purposeIn the interferon era of hepatitis C virus (HCV) therapies, genotype/subtype, cirrhosis, prior treatment failure, sex, and race predicted relapse. Our objective is to validate a targeted proteomics platform of 17 peptides to predict sustained virologic response (SVR). EXPERIMENTAL

designStored plasma from three, open-label, trials of HIV/HCV-coinfected subjects receiving interferon-containing regimens is identified. LC-MS/MS is used to quantitate the peptides directly from plasma, and IL28B genotyping is completed using stored peripheral blood mononuclear cells (PBMC). A logistic regression model is built to analyze the probability of SVR using responders and nonresponders to interferon-based regimens.

resultsThe cohort (N = 35) is predominantly black (51.4%), male (86%), and with median age 48 years. Most patients achieve SVR (54%). Using multivariable models, it is verified that three human corticosteroid binding globulin (CBG) peptides are predictive of SVR in patients with the unfavorable IL28B genotypes (CT/TT). The model performs better than IL28B alone, with an area under the curve of 0.870. CONCLUSIONS AND CLINICAL RELEVANCE: In HIV/HCV-coinfected patients, three human CBG peptides that accurately predict treatment response with interferon-based therapy are identified. This study suggests that a stepwise approach combining a genetic predictor followed by targeted proteomics can improve the accuracy of clinical decision-making.

Indexed as

PharmacogeneticsProteomicsAdultBiomarkersFemaleGenotypeHepatitis CHIV InfectionsHumansInterferon LambdaInterferonsInterleukinsMaleMiddle AgedPolymorphism, GeneticRibavirinBiomarkersInterferon Lambdainterferon-lambda, humanInterferonsInterleukinsRibavirinhepatitis C virushuman Immunodeficiency virusIL28Bpharmacogenomicsproteomics

Identifiers

PMID30058111
PMCPMC6353701

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.